Resources

Here are some helpful resources when scoping a project.


Requirements Definition is the first (and most important) step in any development effort.  This is especially true when the project involves new technologies like Machine Learning, speech or vision.  The Xively toolset gives structure to this stage of the IOT development process.

Xively – Getting Started with IOT  (Adam Michelson, Fraser Macdonald)

Transforming a traditional non-IOT product into a connected IOT device is a nontrivial and risky exercise.  Xively helps refine customer requirements, select tools/platform, and identify interoperability requirements.

Steps in the “journey” are:

  • Conceptualization and Visualization (Model First approach, Product Launcher)
  • Proof of Concept Hardware – point of confusion
  • User Application
  • CRM Integration (Salesforce)
  • Support (of Things)
  • Analytics Integration (entirely another topic)
  • IOT Platform – allowing devices to safely and securely connect

https://www.xively.com/resources/on-demand-webinar-getting-started-with-iot-the-building-blocks-for-a-successful-iot-business

 


Embedded Speech (recognition and synthesis) is a particularly sophisticated and challenging area for embedded development.  A handful of vendors focus on this technology.

Sensory.com   http://www.sensory.com/products/technologies/

Sensory offers world class speech and vision technologies that can be embedded into mobile and other consumer electronic products. The technologies can be implemented across a variety of operating systems and DSPs.

Nervana Systems (Intel)       https://en.wikipedia.org/wiki/Nervana_Systems

Movidius (Intel)   https://www.movidius.com/

Apical (ARM)       http://www.arm.com/products/graphics-and-multimedia

 

 


Research in the area of embedded speech:

MIT Research

Power efficient speech recognition (edge device)  (Price, Glass, Chandrakasan)

http://www.csail.mit.edu/voice_control_everywhere%20

https://pdfs.semanticscholar.org/6392/6d7098a2998fd6e09939096eaefa5f3b72a7.pdf

Learning spoken language phonetic components (Lee, Glass)

http://news.mit.edu/2015/learning-spoken-language-phoneme-data-0914

https://transacl.org/ojs/index.php/tacl/article/view/520/139

 


Cloud data analysis / learning platforms

Peaxy   https://peaxy.net

Large scale data infrastructure that spans on-premise and off-premise.  Gives access to “unstructured” data.  Consolidates data into “data lakes.”

Kafka (Jay Kreps)

Kafka is a data processing architecture focusing on stream (log) data.  An example is Amazon Web Services “topic” publish/subscribe infrastructure.  Kafka address the problem of large quantities of data originating from disparate sources which is needed by a variety of consumers.

The Log: What Every Software Engineer Should Know About Real-time Data’s Unifying Abstraction

Tableau Workbooks (data analytics examples)

https://github.com/cwinsor/tableauSuperstore2/blob/master/superstore2.twbx

 

 

Deep Learning Survey (Cloud-based ML)

Deep Learning Frameworks: A Survey of TensorFlow, Torch, Theano, Caffe, Neon, and the IBM Machine Learning Stack